import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6

## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data

with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)

input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"

Load trained GeneSys model

model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"/workstation/genesys_model_trained_on_root_atlas_20240308_continue4.pth", map_location=torch.device('cpu')))
model = model
model.eval()

ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)

classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}

cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000

## GRN for the transition t1 to t3
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_next(x, pred_h, 0).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 2).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast
Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella

# Save the array to disk
np.save('genesys_ctw_t1-t3.npy', ctw)

ctw = np.load('genesys_ctw_t1-t3.npy')

## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])

## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list

wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")

## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"

pd.Series(wanted_TFs['Name']).value_counts().head(5)

Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network analysis

TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))

def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)

atri = network(0)

tri = network(1)

cor = network(2)

end = network(3)

per = network(4)

pro = network(5)

xyl = network(6)

phl = network(7)

lrc = network(8)

col = network(9)

atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']

## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)

## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)

	atri_betweenness_centrality	tri_betweenness_centrality	lrc_betweenness_centrality	atri_out_centrality	tri_out_centrality	lrc_out_centrality	atri_in_centrality	tri_in_centrality	lrc_in_centrality	centrality_count	centrality_sum
ATS	0.723999	0.808652	0.190379	0.702532	0.555556	0.279898	0.107595	0.163522	0.136132	9	12.668265
WRKY9	0.711599	0.902908	0.024147	0.597046	0.207547	0.208651	0.054852	0.511530	0.020356	9	12.238638
WRKY72	0.875385	0.977688	0.000075	0.069620	0.100629	0.104326	0.122363	0.723270	0.033079	9	12.006434
WRI1	0.944068	0.928228	0.280055	0.196203	0.016771	0.034351	0.236287	0.207547	0.109415	9	11.952926
OFP15	0.889216	0.005483	0.555532	0.128692	0.010482	0.036896	0.156118	0.090147	0.096692	9	10.969258
WRKY27	0.441727	0.786569	0.001702	0.208861	0.020964	0.086514	0.061181	0.140461	0.055980	9	10.803959
GATA17	0.043519	0.327410	0.202125	0.105485	0.020964	0.147583	0.377637	0.220126	0.085242	9	10.530090
AT5G12980	0.259132	0.001462	0.122789	0.181435	0.033543	0.085242	0.059072	0.050314	0.054707	9	9.847696
AT2G36026	0.194659	0.001568	0.014564	0.149789	0.006289	0.064885	0.078059	0.100629	0.038168	9	9.648611

## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)

	atri_betweenness_centrality	tri_betweenness_centrality	atri_out_centrality	tri_out_centrality	atri_in_centrality	tri_in_centrality	centrality_count	centrality_sum
KDR	0.949135	0.922546	0.677215	0.677149	0.683544	0.473795	6	10.383384
ATMYC1	0.163576	0.975882	0.010549	0.408805	0.455696	0.379455	6	8.393963
AGL87	0.657777	0.900186	0.054852	0.044025	0.029536	0.415094	6	8.101471
CRF4	0.696568	0.802120	0.289030	0.090147	0.122363	0.064990	6	8.065217
TRY	0.938118	0.003616	0.267932	0.008386	0.413502	0.039832	6	7.671387
GL2	0.886691	0.020194	0.487342	0.071279	0.156118	0.006289	6	7.627913
RHD6	0.000201	0.798865	0.006329	0.285115	0.164557	0.174004	6	7.429072
AT1G11490	0.012431	0.890831	0.120253	0.100629	0.010549	0.278826	6	7.413519
AT3G53370	0.000004	0.479362	0.025316	0.584906	0.029536	0.213836	6	7.332960
AT2G37120	0.009202	0.283724	0.191983	0.310273	0.018987	0.044025	6	6.858193
FIT	0.066868	0.110072	0.426160	0.117400	0.025316	0.016771	6	6.762589
AT2G02160	0.194178	0.000026	0.056962	0.050314	0.052743	0.016771	6	6.370995
AT5G11340	0.001690	0.021101	0.023207	0.100629	0.094937	0.115304	6	6.356868
AT3G56570	0.002645	0.013675	0.004219	0.004193	0.107595	0.115304	6	6.247631
AT3G51470	0.001615	0.000035	0.056962	0.046122	0.010549	0.056604	6	6.171886

## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	atri_betweenness_centrality	atri_out_centrality	atri_in_centrality	centrality_count	centrality_sum
NAC044	0.874105	0.046414	0.132911	3	4.053429
TTG2	0.841817	0.354430	0.151899	3	4.348146
BNQ3	0.427418	0.316456	0.048523	3	3.792397
AT1G44810	0.171979	0.033755	0.061181	3	3.266916
HDG7	0.094335	0.029536	0.320675	3	3.444546
REF6	0.054759	0.029536	0.054852	3	3.139147
RSZ22a	0.006276	0.027426	0.082278	3	3.115980
bZIP52	0.002658	0.101266	0.004219	3	3.108144
AGL26	0.000847	0.025316	0.052743	3	3.078907
OFP18	0.000397	0.135021	0.023207	3	3.158625
BZR1	0.000156	0.021097	0.059072	3	3.080325
AIP3	0.000058	0.052743	0.054852	3	3.107653
GRF2	0.000049	0.042194	0.061181	3	3.103425
CDC5	0.000013	0.044304	0.023207	3	3.067524

## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	tri_betweenness_centrality	tri_out_centrality	tri_in_centrality	centrality_count	centrality_sum
MYB47	0.929144	0.098532	0.299790	3	4.327467
EGL3	0.833276	0.238994	0.178197	3	4.250467
RSL1	0.813585	0.339623	0.121593	3	4.274800
MBD4	0.775206	0.077568	0.088050	3	3.940824
OFP13	0.743332	0.314465	0.115304	3	4.173101
WRKY75	0.633128	0.264151	0.046122	3	3.943401
ESE3	0.565782	0.498952	0.085954	3	4.150688
AT5G06800	0.303195	0.027254	0.031447	3	3.361895
AT1G02030	0.126993	0.018868	0.010482	3	3.156343
MBF1A	0.075692	0.041929	0.029350	3	3.146971
AT2G17600	0.068614	0.079665	0.048218	3	3.196497
ERF104	0.032341	0.129979	0.058700	3	3.221020
RL6	0.014829	0.159329	0.085954	3	3.260112
AT2G32030	0.011892	0.027254	0.058700	3	3.097845
AT4G39160	0.008117	0.207547	0.044025	3	3.259689
RSL4	0.005919	0.285115	0.027254	3	3.318288
LOL2	0.005580	0.092243	0.077568	3	3.175392
AT5G12850	0.000044	0.035639	0.025157	3	3.060841
AT4G31650	0.000031	0.096436	0.037736	3	3.134203
GL3	0.000026	0.041929	0.050314	3	3.092270
AT1G02040	0.000009	0.111111	0.018868	3	3.129988
AT5G06550	0.000009	0.020964	0.098532	3	3.119506
HMG	0.000004	0.008386	0.014675	3	3.023065
AT5G13920	0.000004	0.054507	0.010482	3	3.064994

## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	lrc_betweenness_centrality	lrc_out_centrality	lrc_in_centrality	centrality_count	centrality_sum
PS1	0.763114	0.184478	0.157761	3	4.105353
WRKY7	0.697755	0.052163	0.123410	3	3.873328
GATA6	0.691802	0.035623	0.131043	3	3.858469
AT1G63100	0.668996	0.034351	0.209924	3	3.913270
CHR24	0.517210	0.052163	0.041985	3	3.611358
DEL3	0.504674	0.089059	0.050891	3	3.644623
AT5G66770	0.423476	0.026718	0.049618	3	3.499812
HDG2	0.309682	0.062341	0.085242	3	3.457265
WRI4	0.253818	0.038168	0.067430	3	3.359416
ATXR5	0.229200	0.026718	0.055980	3	3.311898
KAN	0.209327	0.030534	0.040712	3	3.280574
DAR7	0.184530	0.045802	0.025445	3	3.255777
MIF3	0.068819	0.115776	0.020356	3	3.204951
HMGB4	0.024405	0.082697	0.043257	3	3.150359
NAC063	0.013525	0.045802	0.010178	3	3.069505
3xHMG-box1	0.012444	0.090331	0.054707	3	3.157482
AT3G19360	0.009217	0.010178	0.021628	3	3.041024
NF-YB11	0.005105	0.019084	0.044529	3	3.068718
RR10	0.003199	0.054707	0.027990	3	3.085897
AT5G19490	0.002264	0.020356	0.057252	3	3.079872
HMGB3	0.002096	0.095420	0.104326	3	3.201841
WHY1	0.001993	0.027990	0.094148	3	3.124131
AT4G25400	0.001034	0.104326	0.024173	3	3.129533
TFIIIA	0.000707	0.034351	0.128499	3	3.163557
CHR17	0.000378	0.064885	0.073791	3	3.139054
MBD10	0.000363	0.050891	0.019084	3	3.070338
emb2746	0.000104	0.033079	0.080153	3	3.113335
AT3G48600	0.000018	0.015267	0.054707	3	3.069992
RR3	0.000002	0.026718	0.073791	3	3.100511
CIB5	0.000002	0.063613	0.049618	3	3.113233

## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	col_betweenness_centrality	col_out_centrality	col_in_centrality	centrality_count	centrality_sum
AT3G60670	0.991713	0.345833	0.659722	3	4.997269
IAA20	0.990396	0.268056	0.359722	3	4.618173
RING1	0.984112	0.006944	0.390278	3	4.381334
TRFL3	0.951739	0.052778	0.152778	3	4.157294
NTT	0.951281	0.326389	0.222222	3	4.499892
MAF5	0.950325	0.215278	0.202778	3	4.368380
NAC016	0.919952	0.236111	0.154167	3	4.310230
AT5G25470	0.848657	0.080556	0.097222	3	4.026435
BPC7	0.843063	0.093056	0.245833	3	4.181952
AT2G35910	0.750612	0.219444	0.044444	3	4.014501
AT3G25790	0.345826	0.140278	0.056944	3	3.543048
CRF8	0.328562	0.098611	0.033333	3	3.460506
SUVH3	0.326584	0.009722	0.016667	3	3.352973
FBH4	0.299504	0.406944	0.034722	3	3.741170
KNAT3	0.278384	0.141667	0.027778	3	3.447829
ALY1	0.135646	0.047222	0.016667	3	3.199534
AT1G74250	0.089196	0.026389	0.043056	3	3.158640
AT1G75340	0.074695	0.012500	0.100000	3	3.187195
EIN3	0.060808	0.077778	0.044444	3	3.183030
E2F1	0.051820	0.047222	0.023611	3	3.122653
SR1	0.026808	0.037500	0.045833	3	3.110141
APRR8	0.025607	0.030556	0.012500	3	3.068662
BIM1	0.016340	0.105556	0.045833	3	3.167729
TRFL1	0.012193	0.013889	0.030556	3	3.056637
AT1G77570	0.010145	0.068056	0.036111	3	3.114312
NAC052	0.003498	0.127778	0.040278	3	3.171554
TLP9	0.002028	0.055556	0.006944	3	3.064528
BZIP25	0.001605	0.070833	0.012500	3	3.084939
AT1G21580	0.001429	0.045833	0.068056	3	3.115318
AGL65	0.001234	0.026389	0.075000	3	3.102623
NGA3	0.001221	0.068056	0.061111	3	3.130387
AT4G13040	0.000726	0.033333	0.041667	3	3.075726
SPL14	0.000473	0.045833	0.033333	3	3.079640
HSF A4A	0.000305	0.052778	0.036111	3	3.089194
IDD1	0.000265	0.070833	0.023611	3	3.094709
AL1	0.000249	0.005556	0.073611	3	3.079416
SNL6	0.000240	0.037500	0.033333	3	3.071073
SCL8	0.000236	0.098611	0.009722	3	3.108569
AT1G02080	0.000232	0.025000	0.038889	3	3.064121
ABA1	0.000176	0.069444	0.036111	3	3.105731
AT3G52250	0.000116	0.041667	0.062500	3	3.104283
AT1G19000	0.000099	0.020833	0.043056	3	3.063987
AT3G08505	0.000089	0.056944	0.019444	3	3.076478
BEH2	0.000062	0.022222	0.015278	3	3.037562
AT5G41020	0.000058	0.069444	0.068056	3	3.137558
TLP7	0.000042	0.068056	0.118056	3	3.186154
PRR7	0.000017	0.056944	0.018056	3	3.075017
MYB4R1	0.000015	0.008333	0.013889	3	3.022238
AT3G43240	0.000006	0.006944	0.047222	3	3.054172
AT1G62310	0.000004	0.047222	0.040278	3	3.087504
AT5G16680	0.000002	0.106944	0.009722	3	3.116669
AT4G00270	0.000002	0.030556	0.036111	3	3.066669
NLP7	0.000002	0.065278	0.037500	3	3.102780
RGD3	0.000002	0.027778	0.075000	3	3.102780
TRB1	0.000002	0.030556	0.031944	3	3.062502

## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)

	cor_betweenness_centrality	end_betweenness_centrality	cor_out_centrality	end_out_centrality	cor_in_centrality	end_in_centrality	centrality_count	centrality_sum
ARR3	0.006024	0.000081	0.668675	0.909091	0.319277	0.028571	6	7.931719
OFP12	0.063381	0.000277	0.331325	0.088312	0.018072	0.005195	6	6.506562

## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	cor_betweenness_centrality	cor_out_centrality	cor_in_centrality	centrality_count	centrality_sum
MYB86	0.631508	0.355422	0.144578	3	4.131508
RVN	0.068273	0.036145	0.295181	3	3.399598
SIGE	0.016502	0.234940	0.240964	3	3.492406
AT2G38300	0.010624	0.674699	0.277108	3	3.962432
AT2G42660	0.005988	0.500000	0.349398	3	3.855385
WRKY69	0.005769	0.096386	0.204819	3	3.306973
AT3G61420	0.003980	0.024096	0.066265	3	3.094341
AT1G09250	0.000183	0.054217	0.120482	3	3.174881
ETR2	0.000073	0.060241	0.078313	3	3.138627

## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	end_betweenness_centrality	end_out_centrality	end_in_centrality	centrality_count	centrality_sum
MYB68	0.983543	0.966234	0.189610	3	5.139387
SCR	0.153571	0.792208	0.057143	3	4.002922
MYB36	0.090321	0.981818	0.394805	3	4.466944
BLJ	0.072423	0.883117	0.002597	3	3.958137
RAX2	0.036648	0.922078	0.054545	3	4.013271
AT2G43140	0.028233	0.090909	0.057143	3	3.176285
BIB	0.018419	0.501299	0.075325	3	3.595042
SOM	0.015774	0.854545	0.540260	3	4.410579
AT4G16430	0.005093	0.015584	0.088312	3	3.108989
SUVR5	0.002496	0.012987	0.075325	3	3.090808
GIF2	0.002185	0.002597	0.090909	3	3.095691
AT3G56230	0.000737	0.844156	0.096104	3	3.940997
NAC78	0.000068	0.025974	0.093506	3	3.119548
CIP8	0.000020	0.007792	0.080519	3	3.088332
AT5G57150	0.000014	0.477922	0.124675	3	3.602611
SCL3	0.000007	0.327273	0.135065	3	3.462344

## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)

	per_betweenness_centrality	pro_betweenness_centrality	xyl_betweenness_centrality	phl_betweenness_centrality	per_out_centrality	pro_out_centrality	xyl_out_centrality	phl_out_centrality	per_in_centrality	pro_in_centrality	xyl_in_centrality	phl_in_centrality	centrality_count	centrality_sum
AT3G43430	0.303823	0.799071	0.875056	0.925392	0.904659	0.874142	0.271795	0.835206	0.926327	0.763158	0.165812	0.410112	12	20.054553
MYB20	0.969625	0.989885	0.961050	0.923376	0.861322	0.835240	0.651282	0.044944	0.709642	0.207094	0.211966	0.458801	12	19.824227
BT1	0.736807	0.245415	0.927435	0.751470	0.302275	0.721968	0.695726	0.061798	0.084507	0.094966	0.153846	0.123596	12	16.899809
DAG1	0.924889	0.157642	0.000032	0.852615	0.393283	0.764302	0.020513	0.704120	0.291441	0.390160	0.148718	0.147940	12	16.795655
AT4G24060	0.030895	0.155614	0.919804	0.888406	0.215601	0.581236	0.218803	0.237828	0.302275	0.139588	0.148718	0.222846	12	16.061616
IAA12	0.003116	0.994485	0.984446	0.129498	0.013001	0.612128	0.692308	0.095506	0.062839	0.161327	0.121368	0.073034	12	15.943055
AT5G50010	0.117020	0.968201	0.869772	0.045130	0.132178	0.356979	0.437607	0.069288	0.080173	0.176201	0.102564	0.050562	12	15.405676
IAA13	0.003287	0.124392	0.007792	0.899003	0.109426	0.474828	0.160684	0.544944	0.254605	0.231121	0.225641	0.273408	12	15.309130
AT1G69580	0.001236	0.026561	0.013061	0.861757	0.086674	0.429062	0.210256	0.563670	0.218852	0.110984	0.200000	0.209738	12	14.931850
AT2G29660	0.001078	0.840523	0.062458	0.869574	0.084507	0.236842	0.194872	0.222846	0.044420	0.054920	0.129915	0.059925	12	14.801880
HAT22	0.001481	0.001712	0.898733	0.022472	0.036836	0.140732	0.358974	0.001873	0.048754	0.078947	0.117949	0.749064	12	14.457526
AT5G07580	0.005537	0.932773	0.134185	0.006795	0.124594	0.544622	0.094017	0.026217	0.141928	0.149886	0.073504	0.179775	12	14.413835
IAA11	0.001098	0.781632	0.003272	0.047646	0.151679	0.159039	0.049573	0.529963	0.189599	0.048055	0.191453	0.121723	12	14.274731
SAC51	0.886647	0.000974	0.050779	0.003120	0.041170	0.057208	0.006838	0.020599	0.086674	0.051487	0.102564	0.069288	12	13.377348

## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	per_betweenness_centrality	per_out_centrality	per_in_centrality	centrality_count	centrality_sum
AT3G21330	0.957091	0.096425	0.216685	3	4.270200
AT4G20970	0.478950	0.071506	0.108342	3	3.658798
MGP	0.211395	0.176598	0.042254	3	3.430246
AT2G20100	0.119665	0.231853	0.199350	3	3.550868
MYBC1	0.086822	0.732394	0.645720	3	4.464937
GATA23	0.026388	0.104009	0.412784	3	3.543181
IDD11	0.013838	0.264355	0.123510	3	3.401703
ERF10	0.007827	0.046587	0.120260	3	3.174674
NUC	0.006470	0.242687	0.247021	3	3.496177
bZIP4	0.004337	0.037920	0.191766	3	3.234023
AT5G25475	0.003069	0.003250	0.052004	3	3.058324
LBD14	0.001988	0.147346	0.109426	3	3.258760
LDL1	0.001485	0.011918	0.013001	3	3.026404
SAP	0.001083	0.015168	0.098592	3	3.114843
AT1G62150	0.001065	0.013001	0.029252	3	3.043318
bZIP58	0.000294	0.046587	0.056338	3	3.103219
AT1G20910	0.000186	0.044420	0.024919	3	3.069525
AT1G03040	0.000155	0.099675	0.112676	3	3.212506
LRP1	0.000048	0.052004	0.052004	3	3.104057
SCL1	0.000012	0.045504	0.054171	3	3.099687
AT1G78930	0.000007	0.007584	0.027086	3	3.034677
bHLH104	0.000006	0.063922	0.061755	3	3.125683
BOP2	0.000005	0.017335	0.030336	3	3.047675
AT4G35270	0.000001	0.039003	0.036836	3	3.075841
AT3G13040	0.000001	0.022752	0.032503	3	3.055256
WRKY67	0.000001	0.029252	0.056338	3	3.085592
bHLH11	0.000001	0.015168	0.030336	3	3.045505

## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	pro_betweenness_centrality	pro_out_centrality	pro_in_centrality	centrality_count	centrality_sum
AT3G04850	0.990894	0.086957	0.252860	3	4.330711
HB18	0.954341	0.132723	0.149886	3	4.236950
AT1G75490	0.854318	0.268879	0.156751	3	4.279947
AT4G32800	0.640785	0.020595	0.052632	3	3.714011
NAC080	0.469565	0.254005	0.035469	3	3.759039
WRKY26	0.243053	0.062929	0.069794	3	3.375776
HB24	0.029516	0.025172	0.027460	3	3.082148
BBX18	0.007118	0.002288	0.043478	3	3.052885
AT2G24680	0.003119	0.001144	0.030892	3	3.035156
IDD2	0.002056	0.021739	0.085812	3	3.109608
FRS10	0.001965	0.011442	0.004577	3	3.017983
CRF12	0.001870	0.041190	0.032037	3	3.075097
GAI	0.001687	0.086957	0.101831	3	3.190474
AT5G61590	0.001229	0.040046	0.041190	3	3.082465
ARF19	0.000329	0.030892	0.033181	3	3.064402
AT1G05120	0.000219	0.056064	0.069794	3	3.126077
SPL1	0.000190	0.042334	0.100686	3	3.143211
ERF7	0.000156	0.137300	0.131579	3	3.269035
TRFL6	0.000152	0.020595	0.067506	3	3.088253
AT4G17900	0.000121	0.056064	0.088101	3	3.144285
NTL9	0.000069	0.034325	0.076659	3	3.111053
AT4G22820	0.000063	0.022883	0.050343	3	3.073289
IAA16	0.000025	0.010297	0.117849	3	3.128171
WRKY45	0.000010	0.034325	0.029748	3	3.064084
WRKY2	0.000004	0.012586	0.091533	3	3.104123
WRKY4	0.000003	0.048055	0.042334	3	3.090392
VAL3	0.000003	0.010297	0.020595	3	3.030895
AT1G51200	0.000003	0.006865	0.093822	3	3.100689
AT5G24320	0.000003	0.022883	0.077803	3	3.100689

## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	xyl_betweenness_centrality	xyl_out_centrality	xyl_in_centrality	centrality_count	centrality_sum
TCP10	0.994152	0.297436	0.234188	3	4.525776
VND6	0.991356	0.126496	0.229060	3	4.346912
AT1G26610	0.980570	0.225641	0.135043	3	4.341254
MYB46	0.976780	0.319658	0.211966	3	4.508404
VND1	0.974602	0.716239	0.157265	3	4.848106
...	...	...	...	...	...
AT1G03350	0.000088	0.054701	0.102564	3	3.157353
AT3G19080	0.000035	0.123077	0.039316	3	3.162428
MAF1	0.000023	0.015385	0.046154	3	3.061562
AT3G49930	0.000003	0.010256	0.035897	3	3.046157
SPL7	0.000003	0.015385	0.035897	3	3.051285

63 rows × 5 columns

## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	phl_betweenness_centrality	phl_out_centrality	phl_in_centrality	centrality_count	centrality_sum
APL	0.915639	0.919476	0.217228	3	5.052343
AT4G37180	0.852422	0.823970	0.144195	3	4.820587
AT3G12730	0.823794	0.861423	0.084270	3	4.769487
AT1G49560	0.821043	0.518727	0.091760	3	4.431530
REM22	0.792774	0.224719	0.074906	3	4.092400
CRF1	0.753845	0.297753	0.054307	3	4.105905
DAR2	0.676086	0.837079	0.029963	3	4.543127
HCA2	0.668613	0.696629	0.668539	3	5.033782
AT2G03500	0.664039	0.848315	0.035581	3	4.547934
NAC020	0.582935	0.591760	0.801498	3	4.976193
GATA20	0.457133	0.355805	0.191011	3	4.003949
NAC057	0.388944	0.752809	0.297753	3	4.439506
AT5G41380	0.251713	0.711610	0.121723	3	4.085046
ZFP7	0.235252	0.022472	0.078652	3	3.336376
AIF1	0.225629	0.132959	0.043071	3	3.401659
NAC086	0.207408	0.172285	0.110487	3	3.490180
DOF6	0.176167	0.810861	0.327715	3	4.314744
AT1G26790	0.162876	0.078652	0.026217	3	3.267745
bZIP19	0.056584	0.280899	0.095506	3	3.432988
AT5G02460	0.037313	0.417603	0.159176	3	3.614092
AT1G63820	0.010495	0.464419	0.123596	3	3.598510
AGL15	0.007185	0.131086	0.187266	3	3.325537
WOX2	0.006795	0.024345	0.020599	3	3.051739
AT1G26590	0.004863	0.007491	0.031835	3	3.044188
ARF17	0.004364	0.018727	0.044944	3	3.068034
SIGC	0.003559	0.089888	0.011236	3	3.104683
AT1G47570	0.002920	0.080524	0.127341	3	3.210785
AT5G63700	0.002189	0.368914	0.073034	3	3.444136
SVP	0.001209	0.200375	0.172285	3	3.373868
AT5G05330	0.001156	0.009363	0.022472	3	3.032991
RLT1	0.000362	0.007491	0.046816	3	3.054669
SUVH9	0.000200	0.001873	0.119850	3	3.121923
AL7	0.000148	0.018727	0.082397	3	3.101271
HMGB1	0.000088	0.052434	0.164794	3	3.217316
NF-YC9	0.000056	0.058052	0.164794	3	3.222903
RR2	0.000053	0.041199	0.031835	3	3.073086
BRH1	0.000053	0.170412	0.101124	3	3.271588
ZAP1	0.000035	0.037453	0.035581	3	3.073069
KAN2	0.000018	0.194757	0.074906	3	3.269680
GT2	0.000018	0.052434	0.136704	3	3.189156
AT1G72740	0.000014	0.020599	0.043071	3	3.063684
NF-YB12	0.000014	0.013109	0.043071	3	3.056194
EDA16	0.000014	0.026217	0.119850	3	3.146081
ET2	0.000011	0.048689	0.028090	3	3.076790
NF-YC3	0.000011	0.164794	0.080524	3	3.245329
AT1G28310	0.000007	0.106742	0.082397	3	3.189146
AT4G14920	0.000007	0.005618	0.061798	3	3.067423
PIF7	0.000007	0.033708	0.014981	3	3.048696
VRN2	0.000004	0.005618	0.013109	3	3.018730
AT1G72010	0.000004	0.005618	0.099251	3	3.104872
HB5	0.000004	0.091760	0.099251	3	3.191015
RAP2.2	0.000004	0.033708	0.078652	3	3.112363
MCC1	0.000004	0.003745	0.007491	3	3.011239

Search for individual genes

gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	end_degree_centrality	end_out_centrality	end_in_centrality	end_closeness_centrality	end_eigenvector_centrality	per_degree_centrality	per_out_centrality	per_in_centrality	per_betweenness_centrality	...	pro_out_centrality	pro_in_centrality	pro_closeness_centrality	pro_eigenvector_centrality	xyl_degree_centrality	xyl_out_centrality	xyl_in_centrality	xyl_betweenness_centrality	xyl_closeness_centrality	xyl_eigenvector_centrality
SHR	2.0	0.415584	0.044156	0.371429	0.001088	0.077031	0.280607	0.12026	0.160347	0.002945	...	0.018307	0.08238	0.000571	0.028845	0.198291	0.155556	0.042735	0.800167	0.000618	0.041595

1 rows × 23 columns

gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
BLJ	1.0	0.885714	0.883117	0.002597	0.072423	0.000995	0.121123

gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	cor_degree_centrality	cor_out_centrality	cor_in_centrality	cor_betweenness_centrality	cor_closeness_centrality	cor_eigenvector_centrality	end_degree_centrality	end_out_centrality	end_in_centrality	end_closeness_centrality	end_eigenvector_centrality
JKD	2.0	0.223629	0.2173	0.006329	0.058487	0.001407	0.071245	0.807229	0.716867	0.090361	0.025265	0.000406	0.147346	0.753247	0.633766	0.119481	0.000862	0.110045

gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_closeness_centrality	atri_eigenvector_centrality	cor_degree_centrality	cor_out_centrality	cor_in_centrality	cor_betweenness_centrality	cor_closeness_centrality	cor_eigenvector_centrality	end_degree_centrality	end_out_centrality	end_in_centrality	end_closeness_centrality	end_eigenvector_centrality
RVN	1.0	0.111814	0.111814	0.001211	0.044502	0.331325	0.036145	0.295181	0.068273	0.000349	0.084418	0.106494	0.057143	0.049351	0.00056	0.033328

gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
BIB	1.0	0.576623	0.501299	0.075325	0.018419	0.000829	0.09155

gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	tri_in_centrality	tri_betweenness_centrality	tri_closeness_centrality	tri_eigenvector_centrality	lrc_degree_centrality	lrc_out_centrality	lrc_in_centrality	lrc_closeness_centrality	lrc_eigenvector_centrality
GL2	2.0	0.64346	0.487342	0.156118	0.886691	0.001437	0.121985	0.077568	0.071279	0.006289	0.020194	0.000956	0.037504	0.026718	0.001272	0.025445	0.000548	0.014532

tf_spec.to_csv('TF_GRN_centrality_t1-t3_zscore3.csv', index=True)